Mendelian randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics
Mendelian randomization (MR) is a valuable tool for inferring causal relationships among a wide range of traits using summary statistics from genome-wide association studies (GWASs). Existing summary-level MR methods often rely on strong assumptions, resulting in many false-positive findings. To rel...
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Veröffentlicht in: | Proceedings of the National Academy of Sciences - PNAS 2022-07, Vol.119 (28), p.e2106858119 |
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creator | Hu, Xianghong Zhao, Jia Lin, Zhixiang Wang, Yang Peng, Heng Zhao, Hongyu Wan, Xiang Yang, Can |
description | Mendelian randomization (MR) is a valuable tool for inferring causal relationships among a wide range of traits using summary statistics from genome-wide association studies (GWASs). Existing summary-level MR methods often rely on strong assumptions, resulting in many false-positive findings. To relax MR assumptions, ongoing research has been primarily focused on accounting for confounding due to pleiotropy. Here, we show that sample structure is another major confounding factor, including population stratification, cryptic relatedness, and sample overlap. We propose a unified MR approach, MR-APSS, which 1) accounts for pleiotropy and sample structure simultaneously by leveraging genome-wide information; and 2) allows the inclusion of more genetic variants with moderate effects as instrument variables (IVs) to improve statistical power without inflating type I errors. We first evaluated MR-APSS using comprehensive simulations and negative controls and then applied MR-APSS to study the causal relationships among a collection of diverse complex traits. The results suggest that MR-APSS can better identify plausible causal relationships with high reliability. In particular, MR-APSS can perform well for highly polygenic traits, where the IV strengths tend to be relatively weak and existing summary-level MR methods for causal inference are vulnerable to confounding effects. |
doi_str_mv | 10.1073/pnas.2106858119 |
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Existing summary-level MR methods often rely on strong assumptions, resulting in many false-positive findings. To relax MR assumptions, ongoing research has been primarily focused on accounting for confounding due to pleiotropy. Here, we show that sample structure is another major confounding factor, including population stratification, cryptic relatedness, and sample overlap. We propose a unified MR approach, MR-APSS, which 1) accounts for pleiotropy and sample structure simultaneously by leveraging genome-wide information; and 2) allows the inclusion of more genetic variants with moderate effects as instrument variables (IVs) to improve statistical power without inflating type I errors. We first evaluated MR-APSS using comprehensive simulations and negative controls and then applied MR-APSS to study the causal relationships among a collection of diverse complex traits. The results suggest that MR-APSS can better identify plausible causal relationships with high reliability. In particular, MR-APSS can perform well for highly polygenic traits, where the IV strengths tend to be relatively weak and existing summary-level MR methods for causal inference are vulnerable to confounding effects.</description><identifier>ISSN: 0027-8424</identifier><identifier>ISSN: 1091-6490</identifier><identifier>EISSN: 1091-6490</identifier><identifier>DOI: 10.1073/pnas.2106858119</identifier><identifier>PMID: 35787050</identifier><language>eng</language><publisher>United States: National Academy of Sciences</publisher><subject>Causality ; Genetic diversity ; Genetic Pleiotropy ; Genetic variance ; Genome-wide association studies ; Genome-Wide Association Study ; Genomes ; Mendelian Randomization Analysis - methods ; Phenotype ; Physical Sciences ; Pleiotropy ; Polygenic inheritance ; Randomization ; Reproducibility of Results ; Statistical inference ; Statistics</subject><ispartof>Proceedings of the National Academy of Sciences - PNAS, 2022-07, Vol.119 (28), p.e2106858119</ispartof><rights>Copyright National Academy of Sciences Jul 12, 2022</rights><rights>Copyright © 2022 the Author(s). Published by PNAS 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c421t-e3ebd931179a9404c5b2db60031375d40ecffb65efd3404594fa7088c460cb53</citedby><cites>FETCH-LOGICAL-c421t-e3ebd931179a9404c5b2db60031375d40ecffb65efd3404594fa7088c460cb53</cites><orcidid>0000-0001-5260-3936 ; 0000-0003-1195-9607 ; 0000-0002-9107-8852 ; 0000-0002-2324-6527 ; 0000-0002-4407-3055</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9282238/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9282238/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35787050$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hu, Xianghong</creatorcontrib><creatorcontrib>Zhao, Jia</creatorcontrib><creatorcontrib>Lin, Zhixiang</creatorcontrib><creatorcontrib>Wang, Yang</creatorcontrib><creatorcontrib>Peng, Heng</creatorcontrib><creatorcontrib>Zhao, Hongyu</creatorcontrib><creatorcontrib>Wan, Xiang</creatorcontrib><creatorcontrib>Yang, Can</creatorcontrib><title>Mendelian randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics</title><title>Proceedings of the National Academy of Sciences - PNAS</title><addtitle>Proc Natl Acad Sci U S A</addtitle><description>Mendelian randomization (MR) is a valuable tool for inferring causal relationships among a wide range of traits using summary statistics from genome-wide association studies (GWASs). 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In particular, MR-APSS can perform well for highly polygenic traits, where the IV strengths tend to be relatively weak and existing summary-level MR methods for causal inference are vulnerable to confounding effects.</description><subject>Causality</subject><subject>Genetic diversity</subject><subject>Genetic Pleiotropy</subject><subject>Genetic variance</subject><subject>Genome-wide association studies</subject><subject>Genome-Wide Association Study</subject><subject>Genomes</subject><subject>Mendelian Randomization Analysis - methods</subject><subject>Phenotype</subject><subject>Physical Sciences</subject><subject>Pleiotropy</subject><subject>Polygenic inheritance</subject><subject>Randomization</subject><subject>Reproducibility of Results</subject><subject>Statistical inference</subject><subject>Statistics</subject><issn>0027-8424</issn><issn>1091-6490</issn><issn>1091-6490</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpdkclv1jAQxS1ERT_anrmhSFx6STtesviChCo2qaiX3i3HmXy4Suzghaqc-NPr0FKW00jzfvNmRo-QVxTOKHT8fHU6njEKbd_0lMpnZEdB0roVEp6THQDr6l4wcUhexngDALLp4QU55E3Xd9DAjvz8gm7E2WpXBe1Gv9gfOlnvqsmHyugc9VxZN2FAZ7DSxvjsknX7X_o6o_Up-PWuKrNV1EvpVDGFbFIOWOW4kXt0fsH61o5Fy8uiw11hypaYrInH5GDSc8STx3pErj-8v774VF9effx88e6yNoLRVCPHYZSc0k5qKUCYZmDj0AJwyrtmFIBmmoa2wWnkRW6kmHQHfW9EC2Zo-BF5-2C75mHB0aBLQc9qDXa7R3lt1b-Ks1_V3n9XkvWM8b4YnD4aBP8tY0xqsdHgPGuHPkfFSgTAW0G3XW_-Q298Dq58VyjJJWeCtYU6f6BM8DEGnJ6OoaC2cNUWrvoTbpl4_fcPT_zvNPk9nzGktw</recordid><startdate>20220712</startdate><enddate>20220712</enddate><creator>Hu, Xianghong</creator><creator>Zhao, Jia</creator><creator>Lin, Zhixiang</creator><creator>Wang, Yang</creator><creator>Peng, Heng</creator><creator>Zhao, Hongyu</creator><creator>Wan, Xiang</creator><creator>Yang, Can</creator><general>National Academy of Sciences</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QG</scope><scope>7QL</scope><scope>7QP</scope><scope>7QR</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TK</scope><scope>7TM</scope><scope>7TO</scope><scope>7U9</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H94</scope><scope>M7N</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-5260-3936</orcidid><orcidid>https://orcid.org/0000-0003-1195-9607</orcidid><orcidid>https://orcid.org/0000-0002-9107-8852</orcidid><orcidid>https://orcid.org/0000-0002-2324-6527</orcidid><orcidid>https://orcid.org/0000-0002-4407-3055</orcidid></search><sort><creationdate>20220712</creationdate><title>Mendelian randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics</title><author>Hu, Xianghong ; Zhao, Jia ; Lin, Zhixiang ; Wang, Yang ; Peng, Heng ; Zhao, Hongyu ; Wan, Xiang ; Yang, Can</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c421t-e3ebd931179a9404c5b2db60031375d40ecffb65efd3404594fa7088c460cb53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Causality</topic><topic>Genetic diversity</topic><topic>Genetic Pleiotropy</topic><topic>Genetic variance</topic><topic>Genome-wide association studies</topic><topic>Genome-Wide Association Study</topic><topic>Genomes</topic><topic>Mendelian Randomization Analysis - methods</topic><topic>Phenotype</topic><topic>Physical Sciences</topic><topic>Pleiotropy</topic><topic>Polygenic inheritance</topic><topic>Randomization</topic><topic>Reproducibility of Results</topic><topic>Statistical inference</topic><topic>Statistics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hu, Xianghong</creatorcontrib><creatorcontrib>Zhao, Jia</creatorcontrib><creatorcontrib>Lin, Zhixiang</creatorcontrib><creatorcontrib>Wang, Yang</creatorcontrib><creatorcontrib>Peng, Heng</creatorcontrib><creatorcontrib>Zhao, Hongyu</creatorcontrib><creatorcontrib>Wan, Xiang</creatorcontrib><creatorcontrib>Yang, Can</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Proceedings of the National Academy of Sciences - PNAS</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hu, Xianghong</au><au>Zhao, Jia</au><au>Lin, Zhixiang</au><au>Wang, Yang</au><au>Peng, Heng</au><au>Zhao, Hongyu</au><au>Wan, Xiang</au><au>Yang, Can</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Mendelian randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics</atitle><jtitle>Proceedings of the National Academy of Sciences - PNAS</jtitle><addtitle>Proc Natl Acad Sci U S A</addtitle><date>2022-07-12</date><risdate>2022</risdate><volume>119</volume><issue>28</issue><spage>e2106858119</spage><pages>e2106858119-</pages><issn>0027-8424</issn><issn>1091-6490</issn><eissn>1091-6490</eissn><abstract>Mendelian randomization (MR) is a valuable tool for inferring causal relationships among a wide range of traits using summary statistics from genome-wide association studies (GWASs). Existing summary-level MR methods often rely on strong assumptions, resulting in many false-positive findings. To relax MR assumptions, ongoing research has been primarily focused on accounting for confounding due to pleiotropy. Here, we show that sample structure is another major confounding factor, including population stratification, cryptic relatedness, and sample overlap. We propose a unified MR approach, MR-APSS, which 1) accounts for pleiotropy and sample structure simultaneously by leveraging genome-wide information; and 2) allows the inclusion of more genetic variants with moderate effects as instrument variables (IVs) to improve statistical power without inflating type I errors. We first evaluated MR-APSS using comprehensive simulations and negative controls and then applied MR-APSS to study the causal relationships among a collection of diverse complex traits. The results suggest that MR-APSS can better identify plausible causal relationships with high reliability. In particular, MR-APSS can perform well for highly polygenic traits, where the IV strengths tend to be relatively weak and existing summary-level MR methods for causal inference are vulnerable to confounding effects.</abstract><cop>United States</cop><pub>National Academy of Sciences</pub><pmid>35787050</pmid><doi>10.1073/pnas.2106858119</doi><orcidid>https://orcid.org/0000-0001-5260-3936</orcidid><orcidid>https://orcid.org/0000-0003-1195-9607</orcidid><orcidid>https://orcid.org/0000-0002-9107-8852</orcidid><orcidid>https://orcid.org/0000-0002-2324-6527</orcidid><orcidid>https://orcid.org/0000-0002-4407-3055</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Causality Genetic diversity Genetic Pleiotropy Genetic variance Genome-wide association studies Genome-Wide Association Study Genomes Mendelian Randomization Analysis - methods Phenotype Physical Sciences Pleiotropy Polygenic inheritance Randomization Reproducibility of Results Statistical inference Statistics |
title | Mendelian randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics |
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